About

I believe in learning by doing: building, sharing, gathering feedback, and iterating until success.

I am a data scientist with over ten years in banking and insurance, the last several centered on analytics and machine learning. I have led research and quantitative strategy for global corporate banking, built comprehensive executive dashboards for corporate banking strategy, and developed machine learning forecasting models, liquidity analytic models, and risk analyses — along with the project management to ship them. My models have leveraged cashflow forecast results to drive consistent direct profit growth, surfaced the drivers of customer retention, and recreated and automated Liquidity Assessment (LQA) reporting for an insurance company.

My path wasn't a straight line. I started on the front line of banking, advising clients, then moved into middle office for project and global corporate banking strategy, and along the way earned a master's in financial computing. That mix of client-facing experience, collaboration with both internal and external stakeholders, finance business knowledge, and quantitative and qualitative training shapes how I work: start from the decision someone actually needs to make, then build the model that informs it for practical outcome with direct measurable impact.

Evenings and weekends, I build for myself. Right now I am most interested in building the next generation of AI-driven algorithmic trading systems with frontier technology. This site is where I write up those experiments and to share the results — including the dead ends and the bugs that taught me the most.

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skills

programming
Python · SQL · R
ml / deep learning / llm
PyTorch · TensorFlow · LangChain
data & bi
Power BI · Tableau · Excel
cloud
Azure · GCP · AWS · Databricks · IBM Watson
statistical
SPSS · SAS
financial terminals
Bloomberg · Wind

education

languages

spoken
English · Mandarin · Cantonese